Lua – Torch7 Neural Network Training Error

11 38 20 44 11 38 21 44 29 42 30 44 34 38 6 34 45 42 1
11 38 20 44 11 38 27 44 31 42 18 44 34 38 6 34 45 42 2
6 42 20 44 11 38 21 44 29 42 30 44 34 38 6 34 45 42 3
...
34 40 20 44 11 38 21 44 29 38 30 38 34 45 38 0 0 0 100
...

require'nn'
-- ================================ ====== --
-- Start loading data 
-- ========================== ============ --
print'[INFO] Loading data..'
dataset = {}
function dataset:size() return 10000 end < br />local lin = 1

train_file ='train_10000.t7'
local file = io.open(train_file)
if file then
 for line in file :lines() do
 local input = torch.Tensor(18);
 local output = torch.Tensor(1);

 local X1, X2, X3, X4, X5, X6, X7, X8 , X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, Y = unpack(line:split(" ")) 

 input = {X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18}
 output = Y

 dataset[lin] = {input, output} 
 lin = lin +1 
 end
end
-- ==================== =================== --
-- Create NN 
-- =============== ======================== --
print'[INFO] Creating NN..'
mlp = nn.Sequential( ); - make a multi-layer perceptron
inputs = 18; outputs = 1; HUs = 25; - parameters
mlp:add(nn.Linear(inputs, HUs))
 mlp:add(nn.Tanh())
mlp:add(nn.Linear(HUs, outputs))
-- ================ ====================== --
-- MSE and Training 
-- =========== ============================ --
print'[INFO] MSE and train NN..'
 criterion = nn.MSECriterion() 
trainer = nn.StochasticGradient(mlp, criterion)< br />trainer.learningRate = 0.01
trainer:train(dataset)

# StochasticGradient: training 
/home/yosaikan/torch/install/share/lua/5.1/nn/Linear.lua:34: attempt to call method'dim' (a nil value)
stack traceback:
 /home/yosaikan/torch/install/share/lua/5.1/nn/Linear.lua:34: in function'updateOutput'
 ...e/yosaikan/torch/install/share/lua/5.1/nn /Sequential.lua:25: in function'forward'
 ...an/torch/install/share/lua/5.1/nn/StochasticGradient.lua:35: in function'train'
 iparseSchemeConversion. lua:45: in main chunk
 [C]: in function'f'
 [string "local f = function() return dofile'iparseSch..."]:1: in main chunk
 [C]: in function'xpcall'
 /home/yosaikan/torch/install/share/lua/5.1/itorch/main.lua:174: in function 
 /home/yosaikan/torch/install/share/lua/5.1/lzmq/poller.lua:75: in function'poll'
 .../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:307: in function'poll'
 .../yosaikan/torch/install/share/lua/5.1 /lzmq/impl/loop.lua:325: in function'sleep_ex'
 .../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:370: in function'start '
 /home/yosaikan/torch/install/share/lua/5.1/itorch/main.lua:341: in main chunk
 [C]: in function'require'
 (command line):1: in main chunk
 [C]: at 0x00405980

x001,x002,x003,x004,x005,x006,x007,x008,x009,x010, x011,x012,x013,x014,x015,x016,x017,x018,label

local csv2tensor = require ' csv2tensor'

local inputs = csv2tensor.load("data.csv", {exclude={"label"}})
local labels = csv2tensor.load("data.csv", {include={"label"}})

local dataset = {}

for i=1,inputs:size(1) do
 dataset[i ] = {inputs[i], torch.Tensor{labels[i]}}
end

dataset.size = function(self)
 return inputs:size(1) 
end

-- ...
trainer:train(dataset)< /pre> 

I am trying to implement Here is an example of a neural network. My data is stored in a text file in this form [19 cols x 10000 rows]:

11 38 20 44 11 38 21 44 29 42 30 44 34 38 6 34 45 42 1
11 38 20 44 11 38 27 44 31 42 18 44 34 38 6 34 45 42 2
6 42 20 44 11 38 21 44 29 42 30 44 34 38 6 34 45 42 3
...
34 40 20 44 11 38 21 44 29 38 30 38 34 45 38 0 0 0 100
...

There are tags [100 tags] in the last column.

Use this code:

require'nn'
-- ====================================== --
-- Start loading data 
-- ======================================= --
print'[INFO] Loading data..'
dataset = {}
function dataset:size() return 10000 end 
local lin = 1

train_file = 'train_10000.t7'
local file = io.open(train_file)
if file then
 for line in file:lines() do
 local input = torch.Tensor(18 );
 local output = torch.Tensor(1);

 local X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13 , X14, X15, X16, X17, X1 8, Y = unpack(line:split(" ")) 

 input = {X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13 , X14, X15, X16, X17, X18}
 output = Y

 dataset[lin] = {input, output} 
 lin = lin +1 
 end 
end
-- ======================================= --
-- Create NN 
-- ================================= ===== --
print'[INFO] Creating NN..'
mlp = nn.Sequential(); - make a multi-layer perceptron
inputs = 18; outputs = 1; HUs = 25; - parameters
mlp:add(nn.Linear(inputs, HUs))
mlp:add(nn.Tanh())
mlp:add(nn .Linear(HUs, outputs))
-- =================================== === --
-- MSE and Training 
-- ============================= ========= --
print'[INFO] MSE and train NN..'
criterion = nn.MSECriterion() 
trainer = nn.StochasticGradient(mlp, criterion)
trainer.learningRate = 0.01
trainer:tr ain(dataset)

I received this error message:

# StochasticGradient: training 
/home/yosaikan/torch/install/share /lua/5.1/nn/Linear.lua:34: attempt to call method'dim' (a nil value)
stack traceback:
 /home/yosaikan/torch/install/share/lua/5.1 /nn/Linear.lua:34: in function'updateOutput'
 ...e/yosaikan/torch/install/share/lua/5.1/nn/Sequential.lua:25: in function'forward'
 ...an/torch/install/share/lua/5.1/nn/StochasticGradient.lua:35: in function'train'
 iparseSchemeConversion.lua:45: in main chunk
 [C] : in function'f'
 [string "local f = function() return dofile'iparseSch..."]:1: in main chunk
 [C]: in function'xpcall'
 /home/yosaikan/torch/install/share/lua/5.1/itorch/main.lua:174: in function  
 /home/yosaikan/torch/install/share/lua/5.1/lzmq/poller.lua:75: in function'poll'
 .../yosaikan/torch/install/share/lua/ 5.1/lzmq/i mpl/loop.lua:307: in function'poll'
 .../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:325: in function'sleep_ex'
 .../yosaikan/torch/install/share/lua/5.1/lzmq/impl/loop.lua:370: in function'start'
 /home/yosaikan/torch/install/share/lua/ 5.1/itorch/main.lua:341: in main chunk
 [C]: in function'require'
 (command line):1: in main chunk
 [C]: at 0x00405980 

Can you help me?

Thank you.

I got this error message […] Can you please help me?

In your data set, the input and output should be Tensor-s (the input here is an ordinary Lua table, which is why you get this error, that is, there is no dim method).< /p>

In order to simplify data loading, I suggest you use csv parser, for example, you can use csv2tensor to load data into Tensor.

First make sure to add a header (as the first line) in the file ), such as:

x001,x002,x003,x004,x005,x006,x007,x008,x009,x010,x011,x012,x013,x014,x015,x016, x017,x018,label

Then load your data as follows:

local csv2tensor = require'csv2tensor'

local inputs = csv2tensor.load("data.csv", {exclude={"label"}})
local labels = csv2tensor.load("data.csv", {include={"label"}})

local dataset = {}

for i=1,inputs:size(1) do
 dataset[i] = {inputs[i], torch.Tensor{ labels[i]}}
end

dataset.size = function(self)
 return inputs:size(1)
end

And use this data set for training:

-- ...
trainer:train(dataset)